But we also show that pure (epsilon, 0)-DP is hopeless, even for really simple classes like Gaussians, and that the delta in approx DP is needed to perform unbiased estimation. 7/n
RESEARCH
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Privacy-Bias-Variance Trilemma in Mean Estimators
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Our main result: no. There is a *trilemma* between privacy, bias, and variance of a mean estimator: essentially, one can not simultaneously have strong privacy, low bias, and low variance. This shows the clip-and-noise algorithm is optimal. 5/n
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Unbiased Estimators for Symmetric Distributions
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So that's sad. Is there any hope for special cases? If we happen to know the underlying distribution is symmetric, then *yes*, we can get unbiased estimators. 6/n
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Unbiased Estimators: Better Algorithms for ML
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This can be undesirable in a number of settings, when unbiased estimators are preferred. E.g., then we can compute the statistic multiple times on independent datasets and average them to reduce error. Natural question: are there better algorithms with no (or low) bias? 4/n
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Balancing Bias and Variance in Statistical Estimation Methods
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There's a problem though. By clipping the tails, this approach introduces significant (statistical) bias. Indeed, as we analyze in a paper with @vkerdos @thejonullman (
https://
arxiv.org/abs/2002.09464), we get minimax rates for estimation by *balancing* the bias & variance from noise. 3/n -
Private Mean Estimation: Clipping, Noise, and Differential Privacy
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By now, we understand private mean estimation pretty well, especially in 1D. It turns out that one of the simplest algorithms, taking the empirical mean of the clipped samples (to restrict sensitivity) and adding noise (to introduce privacy) works pretty well. 2/n
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Bias-Variance-Privacy Trilemma in Statistical Estimation
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"A Bias-Variance-Privacy Trilemma for Statistical Estimation," with @argymouz
, Matthew Regehr, @vkerdos, @shortstein
, and @thejonullman
. https://
arxiv.org/abs/2301.13334 Private estimators MUST be biased! 1/n -
INDIAai Launches 2023 AI Forecast Booklet Event
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From advancements in Metaverse technology to extensive growth in generative AI, the year 2023 promises to unlock numerous potentials in various domains. Are you ready to witness them?
— IndiaAI (@OfficialINDIAai) 1 février 2023
Join us for the launch of INDIAai's booklet '23 AI Forecast for 2023' on 3rd Feb, 3 PM. pic.twitter.com/snqw2HfsfiFrom advancements in Metaverse technology to extensive growth in generative AI, the year 2023 promises to unlock numerous potentials in various domains. Are you ready to witness them? Join us for the launch of INDIAai's booklet '23 AI Forecast for 2023' on 3rd Feb, 3 PM.
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AlphaFold Reaches 1M+ Researchers in 18 Months
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Big milestone: 1M+ researchers have now made use of #AlphaFold in their vital work, in the 18 months since we made it freely available.
— Demis Hassabis (@demishassabis) 1 février 2023
From antibiotic resistance to crop sustainability, it's been amazing to see the impact of ‘Science at Digital Speed’: https://t.co/f3SwqXBft8 pic.twitter.com/Tp6XJCJhPFBig milestone: 1M+ researchers have now made use of #AlphaFold in their vital work, in the 18 months since we made it freely available. From antibiotic resistance to crop sustainability, it's been amazing to see the impact of ‘Science at Digital Speed’: https://
unfolded.deepmind.com/#stories -
Google Research Unveils MusicLM, Text-to-Music Generation Model
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Google Research presents MusicLM, a new text-to-audio model https://actuia.com/actualite/google-research-presente-musiclm-un-nouveau-modele-text-to-audio/
… #AI #artificialintelligence #music